Robust simulation functions with disturbance refinement

Simulation functions are Lyapunov-like functions defined over the Cartesian product of state spaces of two (un)perturbed systems, a.k.a., concrete and abstract systems, to relate output trajectories of abstract systems to those of concrete ones while the mismatch between two systems remains within some guaranteed error bounds. In this work, we approximate concrete systems with abstractions with lower dimensions (reduced-order models) and develop robust simulation functions further to consider the perturbation in the abstract system by designing an interface function for the disturbance. The proposed approach allows concrete systems to have large disturbances, which is the case in many real-life applications, while noticeably reducing the closeness error between the two systems. Accordingly, this enables controller design using a reduced-order form of the concrete system and reducing the computational load required for formal synthesis. We demonstrate the efficacy of our approaches on linear control systems by synthesising a formal controller for a 9-state area of the known New England 39-Bus Test System, using only a 3-state abstract system

Let’s Get Physical: Computer Science Meets Systems

In cyber-physical systems (CPS) computing, networking and control (typically regarded as the "cyber" part of the system) are tightly intertwined with mechanical, electrical, thermal, chemical or biological processes (the "physical" part). The increasing sophistication and heterogeneity of these systems requires radical changes in the way sense-and-control platforms are designed to regulate them. In this paper, we highlight some of the design challenges due to the complexity and heterogeneity of CPS. We argue that such challenges can be addressed by leveraging concepts that have been instrumental in fostering electronic design automation while dealing with complexity in VLSI system design. Based on these concepts, we introduce a design methodology whereby platform-based design is combined with assume-guarantee contracts to formalize the design process and enable realization of CPS architectures and control software in a hierarchical and compositional manner. We demonstrate our approach on a prototype design of an aircraft electric power system. © 2014 Springer-Verlag Berlin Heidelberg

A Framework for Automatic Dynamic Constraint Verification in Cyber Physical System Modeling Languages

Design of Cyber-Physical Systems (CPSs) involves overlapping the domains of control theory, network communication, and computational algorithms. Involving multiple domains within the same design greatly increases the system complexity. Furthermore, the physical nature of CPSs generally involves important safety constraints where constraint violations can be catastrophic. The design of CPSs benefits from focusing on the construction of abstracted, high-level models in a DomainSpecific Modeling Language (DSML). A Domain-Specific Modeling Environment (DSME) may aid in the design of such complex systems by enforcing structural design constraints during the construction of models. Models built using a DSME may also use compilers or interpreters to produce real working, low-level artifacts that represent the high-level design. Though each model in a DSME may abide by a formal specification, the behavior of a design may violate dynamic constraints if deployed. Engineers are tasked to ensure that models behave safely by implementing their expert knowledge after using appropriate verification tools. Constraint violations may be eliminated by a modification of the model based on verification feedback, known as Dynamic Constraint Feedback (DCF). Mending such constraint violations is a task generally performed by the model designer. Such a process could potentially be automated through the capture of well-known design practices. The challenging task when automating model correction then becomes in the design of a DSML. A designer of a DSML may have a clear understanding of how to design the syntax and semantics for their domain, but there are no formal methods for implementing verification tools for automatic model correction. Such a framework could greatly aid in the selection of available verification tools, implement well-established design methods, and model dynamic constraints. Presented is the Dynamic Constraint Feedback Metamodeling Language (DCFML), a new metamodel to implement DCF upfront in DSML design. This particular solution provides a concrete solution to the abstraction of the various components of DCF, and then appends them to the DSML design process provided by a DSME

Optimization and Control of Cyber-Physical Vehicle Systems

A cyber-physical system (CPS) is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs) are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined